提出基于基础设施的AMR系统架构，解决工厂物流自动化难题。
Infrastructure-based Autonomous Mobile Robots for Internal Logistics -- Challenges and Future Perspectives
- 构建融合外部传感与本地云的分层智能系统
- 实测验证在重卡制造厂中高效稳定运行
- 适合工业场景下需高可靠性的智能物流系统开发者
自主移动机器人（AMRs）在内部物流中的应用正加速发展，多数方案依赖去中心化的机载智能。然而，在工厂等室内环境中，借助外部传感器和计算资源的基础设施支持系统仍鲜有研究。本文全面综述了基于基础设施的AMR系统，提出一种融合基础设施感知、本地云计算与机载自主的参考架构，并基于该架构回顾了定位、感知与规划的核心技术。我们在一家重型车辆制造企业进行了真实部署，并通过用户体验（UX）评估总结了实际成效。目标是为复杂工业环境中可扩展、鲁棒且人机兼容的AMR系统提供整体发展基础。
原文摘要 · Abstract (English)
The adoption of Autonomous Mobile Robots (AMRs) for internal logistics is accelerating, with most solutions emphasizing decentralized, onboard intelligence. While AMRs in indoor environments like factories can be supported by infrastructure, involving external sensors and computational resources, such systems remain underexplored in the literature. This paper presents a comprehensive overview of infrastructure-based AMR systems, outlining key opportunities and challenges. To support this, we introduce a reference architecture combining infrastructure-based sensing, on-premise cloud computing, and onboard autonomy. Based on the architecture, we review core technologies for localization, perception, and planning. We demonstrate the approach in a real-world deployment in a heavy-vehicle manufacturing environment and summarize findings from a user experience (UX) evaluation. Our aim is to provide a holistic foundation for future development of scalable, robust, and human-compatible AMR systems in complex industrial environments.
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